Sondre U. Solstad
This package provides functions for visualizing AI / Machine Learning.
Written by Sondre U. Solstad, Princeton University (ssolstad@princeton.edu). Let me know if you find this package useful or want to suggest an improvement or feature.
At present, one learning process is supported, namely LASSO regression with cross-validation.
Lasso coordinate descent and cross-validation:
Installation instructions:
library(devtools)
install_github("sondreus/seeAI")
library(seeAI)
library(glmnet)
set.seed(1010)
n=1000;p=100
nzc=trunc(p/10)
x=matrix(rnorm(n*p),n,p)
beta=rnorm(nzc)
fx= x[,seq(nzc)] %*% beta
eps=rnorm(n)*5
y=drop(fx+eps)
px=exp(fx)
px=px/(1+px)
ly=rbinom(n=length(px),prob=px,size=1)
set.seed(1011)
cvob1 <- cv.glmnet(x,y)
animate_glmnet(cvob1)
Line segments are coefficients, outward from the central circle being positive and inward negative. As the complexity penalization parameter (“lambda”) is increased, the size and number of non-zero coefficients decrease. Once the process is complete, a cross-validation procedure selects the ideal model (with the cross-validation error and the standard deviation of this error plotted on the right). Typically, the simplest model with the lowest cross-validation error or an error less than one standard deviation more than the model with the lowest the cross-validation error is preferred.
This package relies on the glmnet package by Jerome Friedman, Trevor Hastie, Noah Simon and Rob Tibshirani, the animation package by Yihui Xie, and ggplot2 developed by Hadley Wickham, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, and Kara Woo.